5G Massive MIMO Research Articles

Design of a 240 elements antenna system for 5G massive MIMO applications with an inclined beam and two operating modes

A massive MIMO (multiple-input-multiple-output) antenna system for 5G base stations is developed, featuring 240 elements and 60 ports. The system supports two modes: individual port operation and network operation. It has a rectangular shape with dimensions of 28×16.8×0.32 cm³ and consists of three layers per side, with 15 ports per layer. Each port contains a 2×2 patch sub-array operating at 3.5 GHz, with beam tilting to generate uncorrelated radiation patterns. The system provides a measured bandwidth of 252 MHz, covering the 3.39–3.64 GHz band. Gain and envelope correlation coefficients are evaluated according to mMIMO parameters. A prototype is fabricated, and the results obtained through CST simulations are validated by experimental measurements.

Deep Learning based Channel Estimation and Hybrid Beamforming for 5G Massive MIMO Wireless Communications

Hybrid beamforming (BF), which divides beamforming operation into radio frequency (RF) and baseband (BB) domains, will play a critical role in MIMO communication at millimeter-wave(mmWave) frequencies. This paper also introduce offline training and prediction schemes for channel estimation and hybrid beamforming. The aim of this paper is that to increase spectral efficiency over more data streams by leveraging the deep learning based LSTM network. The LSTM network is used to train the numeric values from sequence data and predict on new sequence data. The performance is evaluated under different parameters including number of data streams (1, 2, 3 and 4) with different signal-to-noise ratio (SNR) for different carrier frequencies (28GHz, 38GHz, 60GHz and 73GHz) through computer simulation using MATLAB. The simulation results verified that the proposed method can achieve higher spectral efficiency when the number of data streams increases and the value of SNR-Test increases too.

A Low-Cost Compact Dual-Polarized Patch Antenna Array for 5G Massive MIMO Base Station

A Low-Cost Compact Dual-Polarized Patch Antenna Array for 5G Massive MIMO Base Station

Real-Time Optimization of Dynamic Predistortion Model for 5G Massive MIMO Broadband RF Power Amplifier

Real-Time Optimization of Dynamic Predistortion Model for 5G Massive MIMO Broadband RF Power Amplifier

Simplified Methodology of Electromagnetic Field Measurements in the Vicinity of 5G Massive MIMO Base Station for Environmental Exposure Assessment

Simplified Methodology of Electromagnetic Field Measurements in the Vicinity of 5G Massive MIMO Base Station for Environmental Exposure Assessment

Comparative study of kernel algorithms on SIMD vector processor for 5G massive MIMO

Comparative study of kernel algorithms on SIMD vector processor for 5G massive MIMO

Mutual Coupling Effect on Spectral Efficiency of 5G Massive MIMO Millimeter Wave Antenna Array

Mutual Coupling Effect on Spectral Efficiency of 5G Massive MIMO Millimeter Wave Antenna Array

Comparative Study of Kernel Algorithms On SIMD Vector Processor for 5G Massive MIMO

Comparative Study of Kernel Algorithms On SIMD Vector Processor for 5G Massive MIMO

Cell-free Processing for 5G Massive MIMO Deployments: Sleep Modes and User Stream Management

Cell-free Processing for 5G Massive MIMO Deployments: Sleep Modes and User Stream Management

Advancements and Challenges in Scalable Modular Antenna Arrays for 5G Massive MIMO Networks

Advancements and Challenges in Scalable Modular Antenna Arrays for 5G Massive MIMO Networks

A fully integrated wideband GaN Doherty power amplifier MMIC with high efficiency for 5G massive MIMO application

A fully integrated wideband GaN Doherty power amplifier MMIC with high efficiency for 5G massive MIMO application

Massive MIMO in 5G: How Beamforming, Codebooks, and Feedback Enable Larger Arrays

Massive MIMO in 5G: How Beamforming, Codebooks, and Feedback Enable Larger Arrays

Base Station Switching and Resource Allocation for 5G Heterogenous Networks

Enhanced 4th Generation Wireless Network (4G) is called 5th Generation Wireless Network (5G) as it helps in increasing the data rate, capacity and therefore energy efficiency and spectral efficiency of 5G network, in 5G massive MIMO, multiple numbers of antennas are used to transmit the signal with same timefrequency to maximise the number of users, who can communicate with less number of channels. Energy conception is the most dangerous issue in all the generations of wireless networks such as traditional firstgeneration to fifth-generation because of interference, eco signals, fading, packet loss, wastage of bandwidth, remaining energy and security like malicious attacks, blocking whole attacks and wormhole attacks. This efficient research work focused energy-efficient resource allocation scheme based on the shortest job first scheduling algorithm in wireless network (SJF) for the downlink orthogonal frequency division multiple accesses (OFDMA) heterogeneous networks (HetNets) developed. To maximize the spectrum allocation efficiency for the fifth generation (5G) mobile networks, frequency reuse-1 is employed. Thus, advanced inter-cell interference coordination techniques are required to mitigate the inter-cell interference for 5G HetNets. In this paper, the energy-efficient optimization problem based on coordinated scheduling is formulated, which is a shortest path problem and link breakage is intractable to solve directly. The above proposed model was analysed using different parameters energy, bandwidth, Quality of Service (QoS) and interference.

Sintering behavior of 0.95MgTiO3-0.05CaTiO3 ceramics with high densification, high Q and enhanced mechanical properties for 5G massive MIMO technology: Effect of particle gradation

An increase in the requirements for microwave dielectric materials was brought on by the quick growth of communication technologies. In this study, the sintering problems including the narrow sintering densification range and high sintering temperature of 0.95MgTiO3-0.05CaTiO3 (95MCT) ceramics with high densification and high Q, as one of the candidate materials for 5G filters, were discussed and overcome through the particle gradation with sub-micrometric and micrometric powders according to closest packing models. It was found that sub-micrometric (0.7–1 μm) 95MCT powder particles agreed well with the Andreasen model, while micrometric (1–3 μm) particles agreed better with the Dinger-Funk model. With increasing of sub-micrometric powders, the activation energy decreased from 926.01 ± 75.49 to 569.49 ± 31.57 kJ/mol, and the relative density of samples which contained 20–60% fine powders were increased to 98%. The average grain size and grain homogeneity decreased from 7.36 μm to 2.54 μm and 3.36 to 1.79, respectively, to improve the mechanical properties. The results show that the powder grading further improved the comprehensive performance of 95MCT, and the best performance point was J1 which exhibited excellent microwave dielectric properties with εr = 21.1, Qf = 82000 GHz, τf = −0.9 ppm/°C, RD = 98.2%, σf = 178.7 MPa and HV = 8.78 GPa after sintering at 1225 °C for 3h.

Accurate channel estimation and hybrid beamforming using Artificial Intelligence for massive MIMO 5G systems

In a large-scale massive Multi User-Multiple Input Multiple Output (MU-MIMO) environment channel estimation and beamforming is a breathtaking task for enhancing the array gain without utilizing the many Radio Frequency (RF) chains. Somehow, several state-of-the-art works perform channel estimation and Hybrid Beamforming (HB) using Artificial Intelligence (AI) Algorithms but the massive computation intricacy and power consumption hindered the performance of the existing system. By considering the existing issues, we designed a DL-based Hybrid Beamformer for the MIMO environment with 5G communication technology (DLHB-MIMO 5G). The proposed HB design centers on three progressive processes such as accurate channel estimation, hybrid beamformer design, and hybrid beamforming. In the accurate channel estimation phase, the noise and interference-free channels are estimated using the Improved Extreme Learning Machine-Adaptive Orthogonal Matching Pursuit (IELM-AOMP) algorithm based on channel parameters and user feedback. In the HB design stage, the shortcoming of prior DL models is resolved by adopting Transfer Learning Lite Convolutional Neural Network (TL-LiteCNN) for designing a hybrid beamformer. Beforehand, we select the appropriate antenna numbers using Stackelberg Game Theory (StGT) using adequate parameters. In the hybrid beamforming stage, the problem of less Spectral Efficiency (SE) during low SNR conditions is fixed by adopting the Improved Proximal Policy Optimization (IPPO) algorithm with several beamforming parameters to generate highly resourceful hybrid beams. The realization of the proposed research is carried out using the MATLAB R2020a simulation tool and the performance of the proposed work is compared with the major state-of-the-art works in terms of useful performance metrics. The comparative results show that the proposed work beats the existing works.

A Multitask Learning Framework for Pilot Decontamination in 5G Massive MIMO

Reference signals enable the acquisition of channel state information (CSI) for purposes such as channel estimation, beam selection, precoding, and symbol detection in 5G massive multiple-input multipleoutput (MAMIMO) systems. Eventually, as more and more users and cells are added, orthogonal reference signals become few which leads to pilot contamination. Pilot contamination limits the performance and occurs when non-orthogonal reference signals occupy time-frequency resources that are alike. Learning-based techniques have been proposed to alleviate it. However, each can only learn to perform a single task namely pilot assignment, power allocation, pilot design, or de-noising for pilot decontamination. In addition, each learner can only be successful if postulated conditions are met. This study proposes a multitask learning framework that can be trained to dynamically select from the multitude of deep learning models which have been suggested for pilot decontamination. Under all signal-to-noise (SNR) ratios, experiments conducted on the deep residual learning aided channel estimator using the multitask learning framework showed minimum channel estimation errors compared to single-task learning.

5G Massive MIMO and its Impact on Energy Efficiency

5G Massive MIMO and its Impact on Energy Efficiency

Multi‐input fully CNN for joint pilot decontamination and symbol detection in 5G massive MIMO

AbstractThis paper presents a multi‐input deep learning‐based joint pilot decontamination and symbol detection (SD) technique for 5G massive multiple‐input multiple‐output (MAMIMO) systems. It consists of a fully convolutional neural network (FCNN) that finds the 5G channel coefficients using pre‐known sounding reference signals (SRSs) under pilot contamination and a symbol detector derived from projected gradient descent iterations. The study considers pilot contamination caused by inter‐cell interferences between SRSs during channel estimation (CE). The proposed scheme accepts entire 5G orthogonal frequency modulated (OFDM) data and least square estimates and produces the transmitted OFDM signal. Simulation experiments demonstrated that the proposed technique has better CE and SD performance with reduced trainable parameters. Moreover, it is faster due to the lowest elapsed time during end‐to‐end OFDM symbol detection. This paper proposes a joint pilot decontamination and signal detection for 5G MAMIMO systems. It achieves better detection performance with the lowest number of trainable parameters and memory requirements. It is applicable in 5G OFDM symbol detection and channel estimation under pilot contamination.

Bi‐directional LSTM based channel estimation in 5G massive MIMO OFDM systems over TDL‐C model with Rayleigh fading distribution

SummaryIn this work, a deep learning (DL)‐based massive multiple‐input multiple‐output (mMIMO) orthogonal frequency division multiplexing (OFDM) system is investigated over the tapped delay line type C (TDL‐C) model with a Rayleigh fading distribution at frequencies ranging from 0.5 to 100 GHz. The proposed bi‐directional long short‐term memory (Bi‐LSTM) channel state information (CSI) estimator uses online learning during training and offline learning during the practical implementation phase. The design of the estimator takes into account situations in which prior knowledge of channel statistics is limited and targets excellent performance, even with limited pilot symbols (PS). Three separate loss functions (mean square logarithmic error [MSLE], Huber, and Kullback–Leibler Distance [KLD]) are assessed in three classification layers. The symbol error rate (SER) and outage probability performance of the proposed estimator are evaluated using a number of optimization techniques, such as stochastic gradient descent (SGD), momentum, and the adaptive gradient (AdaGrad) algorithm. The Bi‐LSTM‐based CSI estimator is trained considering a specific number of PS. It can be readily seen that by incorporating a cyclic prefix (CP), the system becomes more resilient to channel impairments, resulting in a lower SER. Simulations show that the SGD optimization approach and Huber loss function‐trained Bi‐LSTM‐based CSI estimator have the lowest SER and very high estimation accuracy. By using deep neural networks (DNNs), the Bi‐LSTM method for CSI estimation achieves a superior channel capacity (in bps/Hz) at 10 dB than long short‐term memory (LSTM) and other conventional CSI estimators, such as minimum mean square error (MMSE) and least squares (LS). The simulation results validate the analytical results in the study.

A Reduced-Complexity Load-Modulated MIMO Transmitter Readily Scalable in 5G Massive MIMO Transmitters

This paper presents the design of a four-branch reduced-complexity load-modulated MIMO transmitter. The transmitter is designed to operate at 3 GHz. The transmitter can generate 64-QAM constellations at the input of the radiating elements. The waveforms of the 64-QAM constellations are generated by changing the impedance parameters of the load modulator circuits connected to antennas. At the same time, the output oscillator is kept constant. Thus, a single RF chain can drive the entire transmitter where the power amplifier amplifies a constant signal and does not need to operate in the back-off region. A four-way Wilkinson power divider is used to split the power to the four branches of the transmitter. Microstrip patch antennas have been connected to the load modulators to transmit the desired outputs. RF isolators absorb reflections from each load modulator, allowing branches to generate desired constellations independently and maintaining the system matched at the input all the time. Advanced Design System (ADS) has been used to design, simulate, and fully characterize the transmitter. The proposed transmitter does not require mixers and digital-to-analog converters (DACs). The transmitter has been fabricated, and over-the-air transmission of 64-QAM signals has been tested. The architecture can be scaled to larger array sizes, making it suitable for adoption in 5G massive MIMO systems. High cost, energy, and RF-complexity savings can be achieved if the transmitter is scaled to a larger number of branches.